library(tidyverse)
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library(magrittr)
## 
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##     set_names
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##     extract
datasets::airquality
##     Ozone Solar.R Wind Temp Month Day
## 1      41     190  7.4   67     5   1
## 2      36     118  8.0   72     5   2
## 3      12     149 12.6   74     5   3
## 4      18     313 11.5   62     5   4
## 5      NA      NA 14.3   56     5   5
## 6      28      NA 14.9   66     5   6
## 7      23     299  8.6   65     5   7
## 8      19      99 13.8   59     5   8
## 9       8      19 20.1   61     5   9
## 10     NA     194  8.6   69     5  10
## 11      7      NA  6.9   74     5  11
## 12     16     256  9.7   69     5  12
## 13     11     290  9.2   66     5  13
## 14     14     274 10.9   68     5  14
## 15     18      65 13.2   58     5  15
## 16     14     334 11.5   64     5  16
## 17     34     307 12.0   66     5  17
## 18      6      78 18.4   57     5  18
## 19     30     322 11.5   68     5  19
## 20     11      44  9.7   62     5  20
## 21      1       8  9.7   59     5  21
## 22     11     320 16.6   73     5  22
## 23      4      25  9.7   61     5  23
## 24     32      92 12.0   61     5  24
## 25     NA      66 16.6   57     5  25
## 26     NA     266 14.9   58     5  26
## 27     NA      NA  8.0   57     5  27
## 28     23      13 12.0   67     5  28
## 29     45     252 14.9   81     5  29
## 30    115     223  5.7   79     5  30
## 31     37     279  7.4   76     5  31
## 32     NA     286  8.6   78     6   1
## 33     NA     287  9.7   74     6   2
## 34     NA     242 16.1   67     6   3
## 35     NA     186  9.2   84     6   4
## 36     NA     220  8.6   85     6   5
## 37     NA     264 14.3   79     6   6
## 38     29     127  9.7   82     6   7
## 39     NA     273  6.9   87     6   8
## 40     71     291 13.8   90     6   9
## 41     39     323 11.5   87     6  10
## 42     NA     259 10.9   93     6  11
## 43     NA     250  9.2   92     6  12
## 44     23     148  8.0   82     6  13
## 45     NA     332 13.8   80     6  14
## 46     NA     322 11.5   79     6  15
## 47     21     191 14.9   77     6  16
## 48     37     284 20.7   72     6  17
## 49     20      37  9.2   65     6  18
## 50     12     120 11.5   73     6  19
## 51     13     137 10.3   76     6  20
## 52     NA     150  6.3   77     6  21
## 53     NA      59  1.7   76     6  22
## 54     NA      91  4.6   76     6  23
## 55     NA     250  6.3   76     6  24
## 56     NA     135  8.0   75     6  25
## 57     NA     127  8.0   78     6  26
## 58     NA      47 10.3   73     6  27
## 59     NA      98 11.5   80     6  28
## 60     NA      31 14.9   77     6  29
## 61     NA     138  8.0   83     6  30
## 62    135     269  4.1   84     7   1
## 63     49     248  9.2   85     7   2
## 64     32     236  9.2   81     7   3
## 65     NA     101 10.9   84     7   4
## 66     64     175  4.6   83     7   5
## 67     40     314 10.9   83     7   6
## 68     77     276  5.1   88     7   7
## 69     97     267  6.3   92     7   8
## 70     97     272  5.7   92     7   9
## 71     85     175  7.4   89     7  10
## 72     NA     139  8.6   82     7  11
## 73     10     264 14.3   73     7  12
## 74     27     175 14.9   81     7  13
## 75     NA     291 14.9   91     7  14
## 76      7      48 14.3   80     7  15
## 77     48     260  6.9   81     7  16
## 78     35     274 10.3   82     7  17
## 79     61     285  6.3   84     7  18
## 80     79     187  5.1   87     7  19
## 81     63     220 11.5   85     7  20
## 82     16       7  6.9   74     7  21
## 83     NA     258  9.7   81     7  22
## 84     NA     295 11.5   82     7  23
## 85     80     294  8.6   86     7  24
## 86    108     223  8.0   85     7  25
## 87     20      81  8.6   82     7  26
## 88     52      82 12.0   86     7  27
## 89     82     213  7.4   88     7  28
## 90     50     275  7.4   86     7  29
## 91     64     253  7.4   83     7  30
## 92     59     254  9.2   81     7  31
## 93     39      83  6.9   81     8   1
## 94      9      24 13.8   81     8   2
## 95     16      77  7.4   82     8   3
## 96     78      NA  6.9   86     8   4
## 97     35      NA  7.4   85     8   5
## 98     66      NA  4.6   87     8   6
## 99    122     255  4.0   89     8   7
## 100    89     229 10.3   90     8   8
## 101   110     207  8.0   90     8   9
## 102    NA     222  8.6   92     8  10
## 103    NA     137 11.5   86     8  11
## 104    44     192 11.5   86     8  12
## 105    28     273 11.5   82     8  13
## 106    65     157  9.7   80     8  14
## 107    NA      64 11.5   79     8  15
## 108    22      71 10.3   77     8  16
## 109    59      51  6.3   79     8  17
## 110    23     115  7.4   76     8  18
## 111    31     244 10.9   78     8  19
## 112    44     190 10.3   78     8  20
## 113    21     259 15.5   77     8  21
## 114     9      36 14.3   72     8  22
## 115    NA     255 12.6   75     8  23
## 116    45     212  9.7   79     8  24
## 117   168     238  3.4   81     8  25
## 118    73     215  8.0   86     8  26
## 119    NA     153  5.7   88     8  27
## 120    76     203  9.7   97     8  28
## 121   118     225  2.3   94     8  29
## 122    84     237  6.3   96     8  30
## 123    85     188  6.3   94     8  31
## 124    96     167  6.9   91     9   1
## 125    78     197  5.1   92     9   2
## 126    73     183  2.8   93     9   3
## 127    91     189  4.6   93     9   4
## 128    47      95  7.4   87     9   5
## 129    32      92 15.5   84     9   6
## 130    20     252 10.9   80     9   7
## 131    23     220 10.3   78     9   8
## 132    21     230 10.9   75     9   9
## 133    24     259  9.7   73     9  10
## 134    44     236 14.9   81     9  11
## 135    21     259 15.5   76     9  12
## 136    28     238  6.3   77     9  13
## 137     9      24 10.9   71     9  14
## 138    13     112 11.5   71     9  15
## 139    46     237  6.9   78     9  16
## 140    18     224 13.8   67     9  17
## 141    13      27 10.3   76     9  18
## 142    24     238 10.3   68     9  19
## 143    16     201  8.0   82     9  20
## 144    13     238 12.6   64     9  21
## 145    23      14  9.2   71     9  22
## 146    36     139 10.3   81     9  23
## 147     7      49 10.3   69     9  24
## 148    14      20 16.6   63     9  25
## 149    30     193  6.9   70     9  26
## 150    NA     145 13.2   77     9  27
## 151    14     191 14.3   75     9  28
## 152    18     131  8.0   76     9  29
## 153    20     223 11.5   68     9  30
air<-datasets::airquality
#skimr::skim(air)# General overview of data
#View(air) #Look at your data

air1<-air %>% select(Day, Month, Temp, Temperature=Temp)#Re-organized the columns and renamed 
air2<-air %>%slice(1:10)

air3<-air %>% select(Day, Month,Temp,Temperature=Temp) %>% slice(1:10)

air4<-air %>% select(c(6,5,4), Temperature=Temp) %>% 
    slice(1:10)
air11<-air %>% arrange(Temp)#Ascending order

air12<-air %>%arrange(-Temp)#Descending order
air121<-air %>% arrange(desc(Temp))

air13<-air %>% arrange_at(.vars=c("Temp", "Month"))
air21<-air %>% filter(Month==6)

#air21<-air %>% filter_if()
airm<-air %>% mutate(time=paste(Day, Month,sep=" ")) 
airs<-air %>% summarise(mean(Temp))
airs1<-air %>% summarise_all(mean)

airs1<-air%>% drop_na(Ozone, Solar.R) %>% summarise_all(mean)
airr <-air %>% rename("Solar_Paul"=Solar.R)
ir <- datasets::iris
ir1 <- ir %>% filter(Species=="setosa")  

ir_mean <-ir %>% group_by(Species) %>% 
  summarise_all(mean)

ir_sd<-ir %>% group_by(Species) %>% 
  summarise_all(sd)


#Use fo rbind and cbind
irr <-rbind(ir_mean,ir_sd)
irc <-cbind(ir_mean, ir_sd)

#mean and sd based on species
ir1<- ir %>% group_by(Species) %>% 
  filter(Species=="Setosa") %>% 
  summarise_all(mean)
 library(ggplot2)
paul<-ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point()
# Change point size, color and shape
paul1<-ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(size = 1.2, color = "steelblue", shape = 21)
irir<-ir %>% filter(Species=="setosa") %>% 
gather(key = "sepal_petal",value="length_values",-Species)

#Change from character to numeric
mb <-read_csv("mobilemoney_data.csv")
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   hhid = col_double(),
##   weight = col_double(),
##   account_num = col_double(),
##   age = col_double(),
##   hh_members = col_double()
## )
## See spec(...) for full column specifications.
mb1<-mb %>% mutate(GEN=case_when(gender=="male"~0,                                    gender=="female"~1))

#Make your column entries uniform
#library(plyr)
#library(magrittr)
#a<-factor(mb$gender)
#mb$gender <-mapvalues(a, c("male"), c("MALE")) %>% 
           #mapvalues(a, c("male"), c("MALE"))

,

library(tidyverse)
library(foreign)
library(magrittr)
library(ggplot2)
library(dplyr)
#use of foreign
fin2 = foreign:: read.spss("financial_data.sav",
                        use.value.labels = TRUE,
                        to.data.frame=TRUE)
## re-encoding from CP1252
ggplot(fin2, aes(x=monthly_income, y=age))+
  geom_point(aes(size=Household_size, color="blue"),
             alpha=0.75)+
  xlab("Monthly income")+
  ylab("Age")+
  ggtitle("Age, monthly income and household size")

fin2 = foreign:: read.spss("financial_data.sav",
                        use.value.labels = TRUE,
                        to.data.frame=TRUE)
## re-encoding from CP1252
ggplot(fin2, aes(monthly_income, age, color=Household_size))+
  geom_point(alpha=1.0)+
  xlab("Monthly income")+
  ylab("Age")+
  ggtitle("Age, Monthly income and household size")

fin2 %>% group_by(gender, credit_usage) %>% 
    summarise(count=n()) %>% 
    mutate(percentage=(count/sum(count))*100) %>% 
    ggplot(aes(x=gender,y=credit_usage,color=percentage))+
    geom_point(aes(size=percentage))
## `summarise()` regrouping output by 'gender' (override with `.groups` argument)

fin2 %>% group_by(gender, credit_usage) %>% 
summarise(count=n()) %>% 
mutate(percentage=(count/sum(count))*100) %>% 
ggplot(aes(x=credit_usage,y=percentage,fill=gender))+
    geom_bar(stat="identity", position="dodge")
## `summarise()` regrouping output by 'gender' (override with `.groups` argument)

The bar graph of percentages of the gender in each credit usage category

#geom_boxplot(alpha=1)+ geom_jitter(color=“blue”)

ggplot(fin2,mapping =aes(x=mobile,y=Household_size))+
  geom_boxplot(alpha=1)

iris<-datasets::iris
#Sepal Length vs Species
sl<-ggplot(iris, mapping = aes(x=Species, y=Sepal.Length))+
  geom_boxplot(alpha=0)
  
#Sepal width versus species
sw<-ggplot(iris, mapping = aes(x=Species, y=Sepal.Width))+
  geom_boxplot(alpha=0)

#Petal length versus species
pl<-ggplot(iris, mapping = aes(x=Species, y=Petal.Length))+
  geom_boxplot(alpha=0)

#Petal width versus species
pw<-ggplot(iris, mapping = aes(x=Species, y=Petal.Width))+
  geom_boxplot(alpha=0)
  
library(grid)
library(gridExtra)
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grid.arrange(sl, sw, pl, ncol =2)

library(plotly)
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library(gapminder)#Data set called gapminder/ some packages are basically datasets that have been packaged so.
## Warning: package 'gapminder' was built under R version 4.0.2
gp<-gapminder
df <- gapminder 
fig <- df %>%
  plot_ly(
    x = ~gdpPercap, 
    y = ~lifeExp, 
    size = ~pop, 
    color = ~continent, 
    frame = ~year, 
    text = ~country, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  )
fig <- fig %>% layout(
    xaxis = list(
      type = "log"
    )
  )

fig
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